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Resource investment for DDoS attack resistant SDN: a practical assessment

science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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THE IMPACT OF USING AI AND CHATGPT IN ORGANIZATIONS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

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Annals of Data science 2024年第5期11卷 1563-1580页

作者： Kanapala, Ambedkar Pal, Sukomal Dara, Suresh Jannu, Srikanth Department of Computer Science and Engineering Geethanjali College of Engineering and Technology TS Hyderabad501301 India Varanasi India School of Technology Woxsen University TS Hyderabad502345 India Department of Computer Science and Engineering Vaagdevi Engineering College TS Warangal506005 India

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Information retrieval

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An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

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computers, Materials & Continua 2023年第1期74卷 2097-2113页

作者： Hamayun Khan Irfan Ud Din Arshad Ali Mohammad Husain Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computational systems is changing with the advancement in *** to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded *** operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the *** this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task *** migration avoids peak temperature values in the multicore *** utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature *** technique switches the core bymigrating such task to a core that has less temperature and is in a low power *** proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature *** effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and *** simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Kernel-Based Actor–Critic Learning Framework for Autonomous Brain Control on Trajectory

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IEEE Transactions on Cognitive and Developmental Systems 2025年第3期17卷 554-563页

作者： Song, Zhiwei Zhang, Xiang Chen, Shuhang Tan, Jieyuan Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong

Reinforcement learning (RL)-based brain–machine interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multistep BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this article, we propose a kernel-based actor–critic learning framework for multistep BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial prefrontal cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multistep BC tasks. © 2016 IEEE.

关键词： Adversarial machine learning

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Quad-Mode H-Beam Metasurface-Embedded Dual-Band Circularly-Polarized Multi-Tiered Antenna Scheme With 5G (n79) and Wi-Fi 6E Capabilities

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IEEE Access 2025年 13卷 89844-89857页

作者： Supreeyatitikul, N. Chomtong, P. Konpang, J. Thitimahatthanakusol, P. Akkaraekthalin, P. Rajamangala University of Technology Krungthep Faculty of Engineering Department of Electrical and Telecommunication Engineering Bangkok10120 Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok10800 Thailand Rajamangala University of Technology Isan Faculty of Engineering and Technology Department of Telecommunications Engineering and Digital Innovation Nakhon Ratchasima30000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

This research proposes a quad-mode H-beam metasurface (MTS)-embedded dual-band circularly-polarized (CP) multi-tiered antenna scheme with 5G (n79) and Wi-Fi 6E capabilities. The proposed antenna scheme consists of three layers of corner-truncated FR-4 substrates without an air gap. The upper substrate contains an array of 3× 4 H-beam MTS elemental units tilted at 49°. The middle substrate is a copper plate with a horizontal Z-shaped slot at its center functioning as the ground plane, and the lower substrate contains a microstrip feed line. Characteristic mode analysis (CMA) is employed to optimize the circular polarization of the MTS structure. Results show that Modes 1 & 2 at 4.81 GHz and Modes 4 & 9 at 6.2 GHz contribute to CP radiation. The measured return loss bandwidths of the antenna are 19.37% (4.63 - 5.56 GHz) and 9.51% (5.97 - 6.56 GHz) for the 5G (n79) and Wi-Fi 6E bands. The measured axial ratio bandwidths are 13.33% (4.6 - 5.24 GHz) and 1.77% (6.15 - 6.26 GHz), with maximum gains of 5.2 and 3.9 dBic at 5.5 and 6.2 GHz, respectively. Unlike conventional MTS-based CP antennas which employ rectangular- or square-shaped MTS elements for single-band operation, this work utilizes H-beam-shaped MTS elemental units to achieve dual-band CP capability. Additionally, rather than traditional full-wave methods, CMA is used to optimize circular polarization. Essentially, the proposed antenna scheme is suitable for 5G (n79) and Wi-Fi 6E communications. © 2013 IEEE.

关键词： Circular polarization

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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A Lightweight Object Detection Algorithm for Remote Sensing Images

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engineering Letters 2025年第3期33卷 704-711页

作者： Hou, Donghao Zhang, Yujun Ren, Jia School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou

关键词： Satellite imagery

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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